Autonomous driving certification generalizer

ABSTRACT

Methods, devices and apparatuses pertaining to identifying the candidate road segments for autonomous operations are described. A method may involve obtaining data of a first plurality of road segments that are permitted for one or more autonomous operations. The method may further include generating a road segment template based on the data of the first plurality of road segments, and reconstructing a second plurality of road segments based on the road segment template to obtain a plurality of reconstructed road segments.

CROSS REFERENCE TO RELATED PATENT APPLICATION

The present disclosure is part of a continuation application of U.S.patent application Ser. No. 14/843,514, filed on Sep. 2, 2015, thecontent of which is incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure generally relates to autonomous driving and, moreparticularly, to methods and systems for identifying candidate roadsegments for autonomous operations.

BACKGROUND

The world is at a historic turning point for automotive travel. Therelationship between vehicles and their drivers will likely changesignificantly. Current advancements in automotive technologies havecreated new possibilities for improving highway safety, increasingenvironmental benefits, and expanding mobility. On the other hand,autonomous vehicles (herein referring to vehicles driven not by a humanoperator but by a machine) are still a developing technology, and manycompanies and researchers have speculated about future developments andthe possible effects of the vehicles. Therefore, the regulatory processfor certifying high-level autonomous driving has not converged. It ispossible that high-level autonomous driving may be permitted only inrestricted settings in the near future.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive embodiments of the present disclosureare described with reference to the following figures, wherein likereference numerals refer to like parts throughout the various figuresunless otherwise specified.

FIG. 1 is a diagram depicting an example scheme in which exampleembodiments of the present disclosure may be implemented.

FIG. 2 is a block diagram depicting an example apparatus in accordancewith an embodiment of the present disclosure.

FIG. 3 is a diagram depicting an example road segment template inaccordance with an embodiment of the present disclosure.

FIG. 4 is a diagram depicting another example road segment template inaccordance with an embodiment of the present disclosure.

FIG. 5 is a flowchart depicting an example process in accordance with anembodiment of the present disclosure.

DETAILED DESCRIPTION

In the following description, reference is made to the accompanyingdrawings that form a part thereof, and in which is shown by way ofillustrating specific exemplary embodiments in which the disclosure maybe practiced. These embodiments are described in sufficient detail toenable those skilled in the art to practice the concepts disclosedherein, and it is to be understood that modifications to the variousdisclosed embodiments may be made, and other embodiments may beutilized, without departing from the scope of the present disclosure.The following detailed description is, therefore, not to be taken in alimiting sense.

Since regulatory processes for certifying high-level autonomous drivinghas not converged, high-level autonomy (e.g., National Highway TrafficSafety Administration (NHTSA) levels 3 and 4) may be permitted only inrestricted settings. For example, only a certain number of road segments(e.g., on a whitelist) may be approved as being safe for high-levelautonomous operations. Various factors may render a road segmentamenable to being whitelisted. Implementations herein relate to methodsand systems for extending a whitelist of road segments by identifyingcandidate road segments sharing similar or same properties with respectto existing road segments in the whitelist. In some implementations,road segment templates may be established using, for example,nonparametric clustering and geometric hashing techniques. In theseinstances, the established templates may be used to identify thecandidate road segments.

FIG. 1 illustrates an example scheme 100 in which example embodiments ofthe present disclosure may be implemented. Example scheme 100 includes acomputing system 102. Computing system 102 may include a single serveror a collection of multiple servers in a distributed configuration(e.g., cloud computing service or server farm) or non-distributedconfiguration.

In accordance with various embodiments of the present disclosure,computing system 102 may include various modules, each discussed below.Computing system 102 may obtain data of a number of road segments 104,which include multiple road segments that have been permitted forautonomous operations in an autonomous level. The autonomous level mayinclude, for example, at least one of NHTSA levels 2, 3 or 4. In theseinstances, road segments 104 are approved by a regulatory body (e.g.,NHTSA) as being safe for high-level autonomous operations.

In some implementations, road segments 104 may include multiple roadsegments within a predetermined geographical area, such as a community,a city, a county, a state or province, or a country. In someimplementations, these multiple road segments may be connected to oneanother and/or share similar features. The features may include, forexample, geometry and/or topology of physical roads and/or road networksassociated with road segments 104. Additionally or alternatively, thefeatures may include visibility of lane markers, signage, and/or signalsof road segments 104. Additionally or alternatively, the features mayinclude dominant patterns of traffic of road segments 104.

Computing system 102 may analyze these features of road segments 104 andgenerate one or more road network configurations 106. Further, computingsystem 102 may redefine road network configuration 106 using one or moreconstraints to generate one or more road segment templates 108. In someimplementations, computing system 102 may require a continuity conditionand/or a contiguity condition in generating one or more road segmenttemplates 108. With respect to continuity, multiple road segmenttemplates meeting the continuity condition may be overlaid one overanother and may share boundaries (e.g., having similar geometry). Oneconstraint on the creation of templates may be thought of asguaranteeing complete, or gapless, coverage of the whitelisted roadsegments. That is, a set of road segment templates generated fromwhitelisted road segments need to be able to be used to reconstruct thesame road segments in their entirety, and the reconstruction needs to becontiguous within each road segment in the whitelist. Whether or notmultiple road segment templates may be placed in particular overlappingconfigurations constitutes the evaluation of a continuity constraintwhich, when quantified, may be represented in a binary form (e.g.,having a value of 0 or 1). The degree to which a road segment is coveredin its entirety by reconstruction is the contiguity score (which may benormalized to be in the form of [0, 1]). Accordingly, in implementationsof the present disclosure, it is required that the road segmenttemplates be constructed from the whitelist in a way that contiguoussets of whitelisted road segments can be reconstructed with a contiguityscore of 1. In reconstructing new road segments, computing system 102needs to satisfy the continuity condition (e.g., sharing boundaries) aswell as the contiguity condition (e.g., how well the road segments fittogether and cover a geographical area without any gap between tworeconstructed road segments). In some implementations, a given roadsegment not in the whitelist may be reconstructed from one or more roadsegment templates. For instance, some stretch of a road segment may becontiguous if there are no gaps, which requires that the road segment bereconstructed from road segment templates that overlap. Whether two ormore road segment templates are allowed to overlap in a givenconfiguration is specified by the continuity constraints. In someimplementations, reconstructed road segments may be scored based ontheir contiguity and how well they fit to the road segment templates.Thus, in constructing a new whitelist, computing system 102 may scorecandidate road segments based on how contiguous the candidate roadsegments are and how well they fit the road segment templates, which maybe distributed over different scenes.

Using a road segment template 108, computing system 102 may reconstructroad segments 110 to generate reconstructed road segments 112. In someimplementations, reconstructed road segments 112 may include multipleroad segments sharing features similar to or same as those identifiedfrom road segments 104. In reconstructing new road segments, computingsystem 102 needs to satisfy the continuity condition (e.g., sharingboundaries) as well as the contiguity condition (e.g., how well the roadsegments fit together and cover a geographical area without any gapbetween two reconstructed road segments).

In some implementations, computing system 102 may rank reconstructedroad segments 112 by imposing one or more constraints on reconstructedroad segments 112 to generate ordered and reconstructed road segments114. In some implementations, computing system 102 may schedule a roadtest on a road segment from reconstructed road segments 112 or a roadsegment from the ordered and reconstructed road segments 114. In someimplementations, one or more reconstructed road segments 112 or one ormore ordered and reconstructed road segments 114 may be proposed to oneor more regulatory bodies (e.g., including or in addition to NHTSA) ascandidate road segment(s) to become newly added whitelisted roadsegment(s) for autonomous operations.

FIG. 2 illustrates an example architecture 200 in accordance with anembodiment of the present disclosure. Computing architecture 200 showsadditional details of computing system 102, which may include additionalmodules, kernels, data, and/or hardware.

Computing system 102 may include processor(s) 202 and memory 204. Memory204 may store various modules, applications, programs, or other data.Memory 204 may include one or more sets of instructions that, whenexecuted by the processor(s) 202, cause processor(s) 202 to perform theoperations described herein for computing system 102. Processor(s) 202may include one or more graphics processing units (GPU) and one or morecentral processing units (CPU).

Computing system 102 may have additional features and/orfunctionalities. For example, computing system 102 may also includeadditional data storage devices (removable and/or non-removable)including computer-readable media. Computer-readable media may include,at least, two types of computer-readable media, namely computer storagemedia and communication media. Computer storage media may includevolatile and non-volatile, removable, and non-removable mediaimplemented in any method or technology for storage of information, suchas computer readable instructions, data structures, program modules,program data, or other data. The system memory, the removable storageand the non-removable storage are all examples of computer storagemedia. Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium that can be used to store the desired informationand which can be accessed by computing system 102. Any such computerstorage media may be part of computing system 102. Moreover, thecomputer-readable media may include computer-executable instructionsthat, when executed by the processor(s), perform various functionsand/or operations described herein.

Memory 204 may store an operating system 206 as well as various modulessuch as road segment handler 208, a template generator 210, adetermination module 212, a ranking module 214, a scheduler 216, and areporting module 218 and/or program data 220.

Road segment handler 208 may be configured to analyze multiple roadsegments such as road segments 104 and road segments 110. Suppose thatroad segments 104 have already been approved as being safe forautonomous operations. In this example, the autonomous level may includeat least one of the following: driver assistance, partial automation,conditional automation, high automation, or full automation. In theseinstances, road segments 104 may belong to a whitelist and may includemultiple road segments approved as being safe for high-level autonomousoperations, while road segments 110 have not been whitelisted.Implementations of the present disclosure may identify one or moreparticular road segments in road segments 110 that are sufficientlysimilar to those in road segments 104. These one or more particular roadsegments may be offered as candidates to be added to a new whitelist oran existing whitelist.

In some implementations, road segment handler 208 may obtain data ofroad segments 104 and road segments 110. In these instances, roadsegments 104 are whitelisted as being safe for high-level autonomousoperations, while road segments 110 are not whitelisted. The data ofroad segments 104 may include, for example, statistics on road segments104 pertaining to topological and/or geometrical data, visibility,mobility and/or traffic patterns. In these instances, topological and/orgeometrical data may be obtained using Geographic Information System(GIS) surveys. Data related to visibility may be obtained using cameraand/or a Light Detection and Ranging (LIDAR) system installed onvehicles that have been on road segments 104. Data related to mobilityand traffic patterns may be retrieved from telematics data of vehiclesthat have been on road segments 104.

Template generator 210 may be configured to generate a road segmenttemplate 108 based on the data of road segments 104. In someimplementations, template generator 210 may partition the road segments104 using a nonparametric clustering technique to identify road networkconfiguration 106. The use of nonparametric clustering technique hereinrefers to the use of a clustering technique without pre-defining afixed, finite set of parameters for the clustering. Examples of aclustering technique may include, for example and not limited to, deeplearning based techniques, k-means clustering, and hierarchicalclustering. Road network configuration 106 may include multipleattributes and values corresponding to the multiple attributes. Themultiple attributes may be related to at least one of topology andgeometry, visibility, mobility profiles, traffic patterns, trafficsignage and signals, or environmental factors associated with roadsegments 104.

In some implementations, template generator 201 may impose a constrainton road network configuration 106 to generate road segment template 108.In these instances, the constraint may include a contiguity conditionand/or a continuity condition. For example, the contiguity condition mayindicate connectedness of each of one or more individual road segmentsof road segments 104 with respect to one or more other road segments ofroad segments 104. The continuity condition may indicate a similarity ofmobility profiles of one or more pairs of road segments of road segments104. Imposing a measure of continuity may avoid a situation that a roadsegment is reconstructed from two road segments with different mobilityprofiles (e.g., a school zone and a freeway).

Determination module 212 may be configured to reconstruct road segments110 based on road segment template 108 to generate reconstructed roadsegments 112. Reconstructed road segments 112 may include one or morecandidate road segments sharing one or more properties similar to thoseof road segments 104.

Ranking module 214 may be configured to rank the reconstructed roadsegments 112 based on a contiguity condition. In some implementations,the contiguity condition may indicate connectedness of each of one ormore individual road segments of reconstructed road segments 112 withrespect to one or more other road segments of road segments 112. Inthese instances, reconstructed road segments 112 may be prioritized toobtain one or more road segments for road tests. For example, scheduler216 may be configured to select the one or more road segments fromreconstructed road segments 112, and schedule a road test on the one ormore road segments. Reporting module 218 may be configured to submit theone or more road segments of reconstructed road segments 112 as one ormore candidate road segments to a regulatory body for a permit for theautonomous operations in the autonomous level on the one or morecandidate road segments.

FIG. 3 illustrates an example road segment template 300 in accordancewith an embodiment of the present disclosure. Example road segmenttemplate 300 may be an illustrative, non-limiting implementation of aroad segment template of the one or more road segment templates 108 inexample scheme 100. Example road segment template 300 may include datapertaining to the attributes, parameters and/or characteristics of aroad segment 310. For instance, example road segment template 300 mayspecify the features, geometry, road metrics, speed limit, turnradius/radii, functional class as well as distributions of variousparameters with respect to road segment 310. In the example shown inFIG. 3, example road segment template 300 may specify the features ofroad segment 310 to include a shoulder 320, a first lane 330 adjacentshoulder 320 and for traffic in a first direction, a second lane 340 fortraffic in the first direction, a third lane 360 for traffic in a seconddirection opposite to the first direction, a first road median 350 thatdivides second lane 340 and third lane 360, a second road median 355that divides first lane 330 and third lane 360, a turn radius 390 ofsecond lane 340, a first sidewalk 370 and a second sidewalk 375 adjacentthird lane 360 and extending from both sides of second lane 340 afterthe turn, and a pedestrian crossing 380 across second lane 340 after theturn and connecting first sidewalk 370 and second sidewalk 375.

In some implementations, example road segment template 300 may specifythe grade, curvature and/or surface type of road segment 310. In someimplementations, example road segment template 300 may specify a numberof functional classes to classify road segment 310 to be a freeway(e.g., by classifying road segment 310 as a functional class 1 roadsegment), a highway segment (e.g., by classifying road segment 310 as afunctional class 2 road segment), a city/local driving segment (e.g., byclassifying road segment 310 as a functional class 3 road segment), or arural/dirt road driving segment (e.g., by classifying road segment 310as a functional class 4 road segment). In some implementations, exampleroad segment template 300 may specify a number of distributions ofparameters including, for example, a distribution of speeds of vehiclestraveling on road segment 310, a distribution of occupancy of vehiclestraveling on road segment 310, and a distribution of vehicle models ofvehicles traveling on road segment 310.

FIG. 4 illustrates an example road segment template 400 in accordancewith an embodiment of the present disclosure. Example road segmenttemplate 400 may be an illustrative, non-limiting implementation of aroad segment template of the one or more road segment templates 108 inexample scheme 100. Example road segment template 400 may include datapertaining to the attributes, parameters and/or characteristics of aroad segment 410. For instance, example road segment template 400 mayspecify the features, geometry, road metrics, speed limit, turnradius/radii, functional class as well as distributions of variousparameters with respect to road segment 410. In the example shown inFIG. 4, example road segment template 400 may specify the features ofroad segment 410 to include a strip of grass 420, a first lane 430adjacent the strip of grass 420 and for traffic in a first direction, asecond lane 440 for traffic in the first direction, a third lane 460 fortraffic in a second direction opposite to the first direction, a fourthlane 470 for traffic in the second direction, a first road median 450that divides second lane 440 and third lane 460, a second road median455 that divides first lane 430 and third lane 460, a turn radius 490 ofsecond lane 440, and a bike and pedestrian path 480 adjacent third lane460 and fourth lane 470 and may travel over or under second lane 440after the turn.

In some implementations, example road segment template 400 may specifythe grade, curvature and/or surface type of road segment 410. In someimplementations, example road segment template 400 may specify a numberof functional classes to classify road segment 410 to be a freeway(e.g., by classifying road segment 410 as a functional class 1 roadsegment), a highway segment (e.g., by classifying road segment 410 as afunctional class 2 road segment), a city/local driving segment (e.g., byclassifying road segment 410 as a functional class 4 road segment), or arural/dirt road driving segment (e.g., by classifying road segment 410as a functional class 4 road segment). In some implementations, exampleroad segment template 400 may specify a number of distributions ofparameters including, for example, a distribution of speeds of vehiclestraveling on road segment 410, a distribution of occupancy of vehiclestraveling on road segment 410, and a distribution of vehicle models ofvehicles traveling on road segment 410.

FIG. 5 illustrates an example process 500 implementing an embodiment inaccordance with the present disclosure. Example process 500 may be oneof various implementation scenarios based on example scheme 100, and isprovided solely for illustrative purpose so that those skilled in theart may better appreciate benefits and advantages provided by thepresent disclosure. Therefore, the scope of the present disclosure isnot limited by example process 500. For illustration purpose and notlimiting the scope of the present disclosure, the description of exampleprocess 500 is provided below in the context of implementation usingprocessor 202 of computing apparatus 200.

At 502, processor 202 may obtain data of road segments 104 that arepermitted for autonomous operations in an autonomous level. Theautonomous level may include, for example, at least one of thefollowing: driver assistance, partial automation, conditionalautomation, high automation or full automation. Example process 500 mayproceed from 502 to 504.

At 504, processor 202 may partition road segments 104 using anonparametric clustering technique to identify road networkconfigurations 106. Road network configurations 106 may include multipleattributes and values corresponding to the multiple attributes. Themultiple attributes may relate to at least one of topology, geometry,visibility, mobility profiles, traffic patterns, traffic signage andsignals and/or environmental factors associated with the road segments104. Example process 500 may proceed from 504 to 506.

At 506, processor 202 may impose a constraint on the plurality of roadnetwork configurations 106 to generate a road segment template 108. Theconstraint may include a contiguity condition and/or a continuitycondition. The contiguity condition may indicate connectedness of eachof one or more individual road segments of the road segments 104 withrespect to one or more other road segments of road segments 104. Thecontinuity condition may indicate a similarity of mobility profiles ofone or more pairs of road segments of the road segments 104. Exampleprocess 500 may proceed from 506 to 508.

At 508, processor 202 may obtain data of road segments 110. Roadsegments 110 include road segments that have not been permitted for theautonomous operations in a certain autonomous level, such as NHTSAlevels 2, 3 or 4. Example process 500 may proceed from 508 to 510.

At 510, processor 202 may reconstruct road segments 110 based on roadsegment template 108 to obtain reconstructed road segments 112. In someimplementations, reconstructed road segments 112 may include multipleroad segments sharing features similar to or same as those identifiedfrom road segments 104. Example process 500 may proceed from 510 to 512.

At 512, processor 202 may rank reconstructed road segments 112 based ona contiguity condition indicating connectedness of each of one or moreindividual road segments of reconstructed road segments 110 with respectto one or more other road segments of road segments 110. Example process500 may proceed from 512 to 514.

At 514, processor 202 may select one or more road segments fromreconstructed road segments 112, and schedule a road test on theselected one or more road segments. In some implementations, processor202 may select one or more road segments of reconstructed road segments112 as one or more candidate road segments. Processor 202 may furthersubmit the one or more candidate road segments to a regulatory body fora permit for the autonomous operations.

The articles “a” and “an” are used herein to refer to one or to morethan one (i.e., to at least one) of the grammatical object of thearticle. By way of example, “a user” means one user or more than oneusers. Reference throughout this specification to “one embodiment,” “anembodiment,” “one example,” or “an example” means that a particularfeature, structure, or characteristic described in connection with theembodiment or example is included in at least one embodiment of thepresent disclosure. Thus, appearances of the phrases “in oneembodiment,” “in an embodiment,” “one example,” or “an example” invarious places throughout this specification are not necessarily allreferring to the same embodiment or example. Furthermore, the particularfeatures, structures, databases, or characteristics may be combined inany suitable combinations and/or sub-combinations in one or moreembodiments or examples. In addition, it should be appreciated that thefigures provided herewith are for explanation purposes to personsordinarily skilled in the art and that the drawings are not necessarilydrawn to scale.

Embodiments in accordance with the present disclosure may be embodied asan apparatus, method, or computer program product. Accordingly, thepresent disclosure may take the form of an entirely hardware-comprisedembodiment, an entirely software-comprised embodiment (includingfirmware, resident software, micro-code or the like), or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module,” or “system.” Furthermore,embodiments of the present disclosure may take the form of a computerprogram product embodied in any tangible medium of expression havingcomputer-usable program code embodied in the medium.

The flow diagrams and block diagrams in the attached figures illustratethe architecture, functionality, and operation of possibleimplementations of systems, methods, and computer program productsaccording to various embodiments of the present disclosure. In thisregard, each block in the flow diagrams or block diagrams may representa module, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It will also be noted that each block of the block diagramsand/or flow diagrams, and combinations of blocks in the block diagramsand/or flow diagrams, may be implemented by special purposehardware-based systems that perform the specified functions, operationsor acts, or combinations of special purpose hardware and computerinstructions. These computer program instructions may also be stored ina computer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flow diagram and/orblock diagram block or blocks.

Although the present disclosure is described in terms of certainembodiments, other embodiments will be apparent to those of ordinaryskill in the art, given the benefit of this disclosure, includingembodiments that do not provide all of the benefits and features setforth herein, which are also within the scope of this disclosure. It isto be understood that other embodiments may be utilized, withoutdeparting from the scope of the present disclosure.

The invention claimed is:
 1. A method, comprising: obtaining, by aprocessor of a computing system, data of a first plurality of roadsegments permitted for one or more autonomous operations in anautonomous level, the obtaining of the data of the first plurality ofroad segments comprises operating one or more vehicles on the firstplurality of road segments to collect statistics on the first pluralityof road segments using a Geographic Information System (GIS), a cameraand a Light Detection and Ranging (LIDAR) system, telematics, or acombination thereof, installed on the one or more vehicles; generating,by the processor, a road segment template based on the data of the firstplurality of road segments; obtaining, by the processor, data of asecond plurality of road segments that have not been permitted for theone or more autonomous operations in the autonomous level; andreconstructing, by the processor, the second plurality of road segmentsin the computing system based on the road segment template to provide aplurality of reconstructed road segments in the computing system,wherein the generating of the road segment template based on the firstplurality of road segments comprises partitioning the first plurality ofroad segments using a nonparametric clustering technique to identify aplurality of road network configurations, and wherein the using of thenonparametric clustering technique comprises using deep learning orusing both the deep learning and hierarchical clustering.
 2. The methodof claim 1, wherein the autonomous level comprises driver assistance,partial automation, conditional automation, high automation, fullautomation, or a combination thereof.
 3. The method of claim 1, whereinthe generating of the road segment template based on the first pluralityof road segments further comprises: imposing a constraint on theplurality of road network configurations to generate the road segmenttemplate.
 4. The method of claim 3, wherein the plurality of roadnetwork configurations comprise a plurality of attributes and aplurality of values corresponding to the plurality of attributes.
 5. Themethod of claim 4, wherein the plurality of attributes are associatedwith at least one of topology, geometry, visibility, mobility profiles,traffic signage and signals, or environmental factors associated withthe first plurality of road segments.
 6. The method of claim 3, whereinthe constraint comprises a contiguity condition, and wherein thecontiguity condition indicates connectedness of each of one or moreindividual road segments of the first plurality of road segments withrespect to one or more other road segments of the first plurality ofroad segments.
 7. The method of claim 3, wherein the constraintcomprises a continuity condition, and wherein the continuity conditionindicates a similarity of mobility profiles of two road segments of thefirst plurality of road segments.
 8. The method of claim 1, furthercomprising: submitting one or more road segments of the plurality ofreconstructed road segments as one or more candidate road segments to areceiving party.
 9. The method of claim 1, further comprising: rankingthe plurality of reconstructed road segments based on a contiguitycondition indicating connectedness of each of one or more individualroad segments of the plurality of reconstructed road segments.
 10. Themethod of claim 1, further comprising: selecting one or more roadsegments from the plurality of reconstructed road segments; andscheduling a road test on the selected one or more road segments.
 11. Amethod, comprising: obtaining, by a processor of a computing system,data of a first plurality of road segments permitted for one or moreautonomous operations in an autonomous level, the obtaining of the dataof the first plurality of road segments comprises operating one or morevehicles on the first plurality of road segments to collect statisticson the first plurality of road segments; generating, by the processor, aroad segment template based on the data of the first plurality of roadsegments; obtaining, by the processor, data of a second plurality ofroad segments that have not been permitted for the one or moreautonomous operations in the autonomous level; and reconstructing, bythe processor, the second plurality of road segments in the computingsystem based on the road segment template to provide a plurality ofreconstructed road segments in the computing system, wherein theautonomous level comprises driver assistance, partial automation,conditional automation, high automation, full automation, or acombination thereof, wherein the generating of the road segment templatebased on the first plurality of road segments comprises partitioning thefirst plurality of road segments using a nonparametric clusteringtechnique to identify a plurality of road network configurations, andwherein the using of the nonparametric clustering technique comprisesusing deep learning or using the deep learning and hierarchicalclustering.
 12. The method of claim 11, wherein the generating of theroad segment template based on the first plurality of road segmentsfurther comprises: imposing a constraint on the plurality of roadnetwork configurations to generate the road segment template.
 13. Themethod of claim 12, wherein the plurality of road network configurationscomprise a plurality of attributes and a plurality of valuescorresponding to the plurality of attributes.
 14. The method of claim13, wherein the plurality of attributes are associated with at least oneof topology, geometry, visibility, mobility profiles, traffic signageand signals, or environmental factors associated with the firstplurality of road segments.
 15. The method of claim 12, wherein theconstraint comprises a contiguity condition, and wherein the contiguitycondition indicates connectedness of each of one or more individual roadsegments of the first plurality of road segments with respect to one ormore other road segments of the first plurality of road segments. 16.The method of claim 12, wherein the constraint comprises a continuitycondition, and wherein the continuity condition indicates a similarityof mobility profiles of two road segments of the first plurality of roadsegments.
 17. The method of claim 11, further comprising: submitting oneor more road segments of the plurality of reconstructed road segments asone or more candidate road segments to a receiving party.
 18. The methodof claim 11, further comprising: ranking the plurality of reconstructedroad segments based on a contiguity condition indicating connectednessof each of one or more individual road segments of the plurality ofreconstructed road segments.
 19. The method of claim 11, furthercomprising: selecting one or more road segments from the plurality ofreconstructed road segments; and scheduling a road test on the selectedone or more road segments.